{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import necessary dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import text_normalizer as tn\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load and normalize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                              review sentiment\n",
      "0  One of the other reviewers has mentioned that ...  positive\n",
      "1  A wonderful little production. <br /><br />The...  positive\n",
      "2  I thought this was a wonderful way to spend ti...  positive\n",
      "3  Basically there's a family where a little boy ...  negative\n",
      "4  Petter Mattei's \"Love in the Time of Money\" is...  positive\n"
     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv(r'movie_reviews.csv')\n",
    "\n",
    "# take a peek at the data\n",
    "print(dataset.head())\n",
    "reviews = np.array(dataset['review'])\n",
    "sentiments = np.array(dataset['sentiment'])\n",
    "\n",
    "# build train and test datasets\n",
    "train_reviews = reviews[:35000]\n",
    "train_sentiments = sentiments[:35000]\n",
    "test_reviews = reviews[35000:]\n",
    "test_sentiments = sentiments[35000:]\n",
    "\n",
    "# normalize datasets\n",
    "norm_train_reviews = tn.normalize_corpus(train_reviews)\n",
    "norm_test_reviews = tn.normalize_corpus(test_reviews)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extract features from positive and negative reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25000, 331) (25000, 331)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "# consolidate all normalized reviews\n",
    "norm_reviews = norm_train_reviews+norm_test_reviews\n",
    "# get tf-idf features for only positive reviews\n",
    "positive_reviews = [review for review, sentiment in zip(norm_reviews, sentiments) if sentiment == 'positive']\n",
    "ptvf = TfidfVectorizer(use_idf=True, min_df=0.05, max_df=0.95, ngram_range=(1,1), sublinear_tf=True)\n",
    "ptvf_features = ptvf.fit_transform(positive_reviews)\n",
    "# get tf-idf features for only negative reviews\n",
    "negative_reviews = [review for review, sentiment in zip(norm_reviews, sentiments) if sentiment == 'negative']\n",
    "ntvf = TfidfVectorizer(use_idf=True, min_df=0.05, max_df=0.95, ngram_range=(1,1), sublinear_tf=True)\n",
    "ntvf_features = ntvf.fit_transform(negative_reviews)\n",
    "# view feature set dimensions\n",
    "print(ptvf_features.shape, ntvf_features.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topic Modeling on Reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pyLDAvis\n",
    "import pyLDAvis.sklearn\n",
    "from sklearn.decomposition import NMF\n",
    "import topic_model_utils as tmu\n",
    "\n",
    "pyLDAvis.enable_notebook()\n",
    "total_topics = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display and visualize topics for positive reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic #1 without weights\n",
      "['like', 'not', 'think', 'really', 'say', 'would', 'get', 'know', 'thing', 'much', 'bad', 'go', 'lot', 'could', 'even']\n",
      "\n",
      "Topic #2 without weights\n",
      "['movie', 'see', 'watch', 'great', 'good', 'one', 'not', 'time', 'ever', 'enjoy', 'recommend', 'make', 'acting', 'like', 'first']\n",
      "\n",
      "Topic #3 without weights\n",
      "['show', 'episode', 'series', 'tv', 'watch', 'dvd', 'first', 'see', 'time', 'one', 'good', 'year', 'remember', 'ever', 'would']\n",
      "\n",
      "Topic #4 without weights\n",
      "['performance', 'role', 'play', 'actor', 'cast', 'good', 'well', 'great', 'character', 'excellent', 'give', 'also', 'support', 'star', 'job']\n",
      "\n",
      "Topic #5 without weights\n",
      "['man', 'young', 'old', 'two', 'get', 'year', 'woman', 'take', 'go', 'come', 'find', 'back', 'girl', 'father', 'friend']\n",
      "\n",
      "Topic #6 without weights\n",
      "['film', 'see', 'one', 'scene', 'make', 'not', 'time', 'director', 'horror', 'music', 'many', 'cinema', 'release', 'work', 'use']\n",
      "\n",
      "Topic #7 without weights\n",
      "['story', 'tell', 'character', 'true', 'book', 'well', 'line', 'base', 'interesting', 'end', 'simple', 'read', 'beautiful', 'main', 'different']\n",
      "\n",
      "Topic #8 without weights\n",
      "['funny', 'comedy', 'laugh', 'humor', 'fun', 'moment', 'line', 'not', 'guy', 'get', 'make', 'lot', 'one', 'time', 'show']\n",
      "\n",
      "Topic #9 without weights\n",
      "['life', 'world', 'people', 'us', 'real', 'live', 'human', 'war', 'many', 'show', 'not', 'way', 'no', 'make', 'feel']\n",
      "\n",
      "Topic #10 without weights\n",
      "['love', 'fall', 'song', 'wonderful', 'beautiful', 'music', 'heart', 'girl', 'would', 'watch', 'great', 'favorite', 'always', 'family', 'woman']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# build topic model on positive sentiment review features\n",
    "pos_nmf = NMF(n_components=total_topics, \n",
    "          random_state=42, alpha=0.1, l1_ratio=0.2)\n",
    "pos_nmf.fit(ptvf_features)      \n",
    "# extract features and component weights\n",
    "pos_feature_names = ptvf.get_feature_names()\n",
    "pos_weights = pos_nmf.components_\n",
    "# extract and display topics and their components\n",
    "pos_topics = tmu.get_topics_terms_weights(pos_weights, pos_feature_names)\n",
    "tmu.print_topics_udf(topics=pos_topics,\n",
    "                 total_topics=total_topics,\n",
    "                 num_terms=15,\n",
    "                 display_weights=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el638028532217185605635571818\"></div>\n",
       "<script type=\"text/javascript\">\n",
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-4.8886, -4.6821, -4.077, -5.1447, -4.9197, -4.6349, -4.7732, -4.7286, -5.1194, -5.0235, -4.1624, -4.3291, -4.8554, -4.2555, -4.4994, -4.3456, -4.3232, -4.5287, -4.2747, -4.3926, -4.4761, -4.4896, -4.5762, -2.4048, -4.5337, -4.7521, -5.0384, -5.1305, -4.5809, -4.4871, -5.0595, -4.9734, -5.2193, -5.2047, -5.0012, -5.4769, -5.0894, -5.0884, -4.4807, -4.104, -4.8904, -3.7587, -4.6715, -4.5098, -4.055, -4.1948, -4.288, -4.5328, -4.626, -4.2837, -1.9294, -4.094, -4.5372, -3.7906, -3.9605, -3.2753, -3.019, -4.4769, -4.6827, -4.2582, -4.7758, -3.3315, -4.4069, -4.5186, -4.7906, -3.4013, -3.684, -3.5631, -3.5895, -4.2201, -4.3527, -4.3999, -3.5473, -3.0609, -4.1471, -3.6974, -4.1759, -3.802, -4.1158, -4.5869, -3.5476, -4.8782, -4.7766, -5.208, -4.7382, -4.8985, -5.0678, -4.7885, -4.9764, -4.3229, -4.4407, -4.4021, -4.5546, -4.3842, -4.4475, -4.3971, -3.2031, -3.151, -4.0367, -3.4313, -3.244, -3.2209, -4.2886, -3.8032, -4.3637, -4.3395, -4.3461, -4.2321, -4.1035, -4.5013, -4.6718, -3.6224, -3.9614, -3.539, -3.6382, -3.7356, -4.0343, -1.7266, -3.103, -3.9604, -3.7185, -3.7135, -4.3198, -4.0381, -3.8795, -4.3967, -4.3464, -3.162, -4.4845, -4.9984, -4.7552, -4.5912, -3.7875, -4.2311, -4.3597, -2.7741, -2.7572, -3.2576, -2.5275, -3.9591, -4.3962, -5.0546, -4.535, -4.4705, -3.9723, -5.2406, -4.8546, -4.9813, -4.8124, -4.5925, -3.7869, -4.8011, -4.3923, -4.5, -4.1503, -4.6404, -4.447, -4.1163, -4.2024, -4.2913, -4.4665, -1.5138, -3.4273, -3.4744, -3.4782, -3.881, -3.4927, -3.6499, -4.0086, -4.6262, -4.1546, -4.7967, -4.8028, -4.2969, -4.253, -4.6678, -4.2355, -4.4473, -4.4776, -4.113, -4.1228, -4.1417, -1.763, -2.0867, -2.3345, -3.4352, -3.512, -3.9735, -4.3539, -4.213, -4.9672, -5.2037, -5.1813, -4.6445, -5.3487, -4.8612, -4.5093, -4.8198, -4.4324, -4.2663, -4.4647, -4.5748, -4.5978, -4.5551], \"loglift\": [15.0, 14.0, 13.0, 12.0, 11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 1.6882, 1.6731, 1.6352, 1.5759, 1.4913, 1.4797, 1.4632, 1.4598, 1.4557, 1.4022, 1.3957, 1.3866, 1.3058, 1.3045, 1.2872, 1.2368, 1.2606, 1.1867, 1.0603, 0.9797, 0.661, 0.995, 1.1, 1.0545, 0.9337, 1.7904, 1.774, 1.7629, 1.7421, 1.679, 1.6439, 1.6431, 1.6184, 1.6171, 1.5934, 1.567, 1.5589, 1.5567, 1.5451, 1.4998, 1.4782, 1.4916, 1.1856, 1.2429, 1.0974, 1.0158, 1.2447, 0.746, 0.8113, 0.9356, 0.7858, 1.2024, 1.9297, 1.8278, 1.6599, 1.5522, 1.4958, 1.4784, 1.4641, 1.4272, 1.4229, 1.3545, 1.3056, 1.2742, 1.2354, 1.2015, 1.1988, 1.1679, 1.0992, 1.1602, 0.6836, 1.0634, 0.954, 0.5206, 0.5701, 0.4881, 0.7465, 0.8336, 0.0334, 2.1028, 1.4978, 1.4724, 1.4623, 1.4456, 1.4427, 1.4233, 1.3653, 1.352, 1.3387, 1.3278, 1.3183, 1.2946, 1.1939, 1.1081, 1.104, 1.0922, 1.0126, 0.7276, 0.5448, 0.472, 0.8108, 2.1983, 2.1762, 2.0929, 2.0541, 1.9055, 1.8521, 1.8096, 1.7929, 1.7499, 1.6449, 1.5439, 1.4215, 1.354, 1.3535, 1.3444, 1.2368, 1.3074, 0.9564, 0.9887, 0.9238, 0.9668, 0.4282, 0.3174, -0.08, 2.2507, 2.2084, 2.207, 2.077, 2.0566, 1.9789, 1.9561, 1.8552, 1.8497, 1.8355, 1.799, 1.6748, 1.5433, 1.5385, 1.5167, 1.3318, 1.3764, 0.9664, 1.0116, 1.0348, 1.0742, 2.5823, 2.2984, 2.1059, 2.1037, 1.9265, 1.8628, 1.7829, 1.7405, 1.6847, 1.678, 1.6084, 1.4358, 1.4138, 1.3403, 1.3251, 1.1668, 1.1833, 1.1265, 2.6012, 2.6012, 2.4154, 2.2849, 1.7072, 1.5025, 1.4311, 1.3895, 1.2843, 1.2384, 1.2349, 1.2048, 1.1753, 1.1548, 1.1498, 0.9311, 1.1157, 0.9467, 0.9662, 0.6258, 1.0252, 0.8059, 0.3259, 0.3733, 0.214, 0.5765, 2.7351, 2.3726, 2.333, 2.0793, 2.0448, 1.9936, 1.814, 1.7648, 1.5555, 1.5469, 1.5408, 1.5374, 1.5104, 1.4374, 1.4358, 1.3944, 1.3739, 1.3011, 0.93, 0.5952, 0.5081, 2.9068, 2.8337, 2.8311, 2.4865, 2.0664, 1.8538, 1.5899, 1.4069, 1.3244, 1.2751, 1.2639, 1.2196, 1.2096, 1.1179, 1.1124, 0.9589, 0.5884, 0.0508, 0.3003, 0.2014, 0.2146, 0.0206], \"Total\": [2065.0, 1945.0, 2565.0, 1355.0, 1055.0, 1895.0, 825.0, 1175.0, 681.0, 669.0, 768.0, 680.0, 619.0, 798.0, 652.0, 211.102331599872, 317.11151730942265, 419.5110141829647, 643.7002892393481, 227.33047325119514, 828.4647066754853, 543.5421915363412, 253.05566502446985, 850.0345733326526, 393.0797927062029, 342.6819740078498, 183.89845211132385, 301.19151852355265, 523.3683857637286, 379.2717488199495, 1161.39812448163, 671.9136452050499, 707.0312012592906, 933.5586385787474, 954.6393576563172, 1929.4283950170275, 794.8362847493763, 609.3118901284057, 612.7191624690993, 691.579667112326, 229.5285354760118, 263.3390208650203, 566.5338914484261, 223.6117520300187, 203.24664683472508, 258.80161351915524, 474.3332388161554, 167.1648480569287, 209.60741325908413, 285.3530585067771, 255.1489717178991, 268.9648085652604, 182.36542752620477, 203.05131356687608, 502.586581499657, 434.7417273581025, 253.42402088315544, 627.0112788473533, 463.94420439110706, 625.8203821078462, 694.4148294702152, 449.72609150877395, 954.6393576563172, 794.8362847493763, 645.6932344483944, 740.0107430763026, 447.3655877017358, 1895.9531930218877, 249.76642469669235, 237.46322457551523, 198.62773250859019, 191.67620070613646, 337.88727430488643, 376.48071771952453, 220.36932765914412, 241.23292652404865, 201.99590081150728, 215.2195130618619, 272.222690334722, 175.86983870731785, 268.04806681098654, 269.04389984134775, 509.5080085163802, 795.412013777677, 340.8406264908757, 1702.3615811240238, 467.35604602875986, 612.877818416905, 1489.8367163701364, 1232.861491227647, 1219.1244461195154, 737.1412740145689, 615.5462482999756, 1929.4283950170275, 2565.110732260605, 539.2663334123499, 355.103342291353, 756.83701219722, 649.372221589171, 1292.1081637847835, 1702.3615811240238, 419.8307516923909, 346.2977142286558, 536.58010046348, 323.2598900922538, 1383.2926515727406, 483.258561722503, 477.97272054213744, 396.77595700351634, 1598.2987164089782, 1219.1244461195154, 1489.8367163701364, 1929.4283950170275, 1232.861491227647, 1161.39812448163, 789.4595394257756, 462.4048145823697, 768.9294912401956, 282.03108806487177, 459.69424870918954, 330.48284945670434, 506.7140902774977, 386.3287848681242, 245.23909850140024, 723.7915926983442, 212.5059919354691, 260.22120795285184, 191.05922465517992, 326.9663921103953, 278.65502382645445, 237.42255415302202, 349.6100402682419, 269.9656540805356, 737.1412740145689, 634.3737543658107, 703.5136463285016, 578.6069417200321, 1175.6911655015595, 1232.861491227647, 1929.4283950170275, 619.0417799673268, 680.4106967099755, 281.0010603974041, 586.2523249733761, 721.5763729838645, 798.1275090357276, 280.7272086065002, 504.54281435623574, 289.64683948818686, 300.97840255657803, 310.102752481873, 393.5130919064794, 510.3970243200657, 344.55698882976515, 296.9272025177769, 1020.2588605039222, 695.2772447729777, 1598.2987164089782, 1383.2926515727406, 1226.1819134814962, 874.3660983448092, 1945.2670328408099, 652.3896148249491, 335.53375987106205, 428.3196154200863, 513.8898193034221, 298.7173695303069, 428.8511637246433, 524.3294900359895, 330.5292892573337, 349.9242658644162, 1226.1819134814962, 388.2785397094541, 237.42255415302202, 325.89653583894847, 389.86794860175917, 1020.2588605039222, 643.9657932563802, 599.3348404311233, 669.636091338051, 681.0532872354679, 497.2309394783012, 1175.6911655015595, 500.5567029153305, 396.77595700351634, 220.58142980998758, 386.65651051529534, 458.1498417114189, 789.4595394257756, 222.85081751671328, 337.88727430488643, 306.57441012612406, 370.5050299606022, 463.94420439110706, 1292.1081637847835, 389.64407801495304, 694.4148294702152, 611.4627257024349, 1219.1244461195154, 500.9017967127385, 756.83701219722, 1702.3615811240238, 1489.8367163701364, 1598.2987164089782, 933.5586385787474, 2065.7225750124644, 437.9629880891077, 434.70915389039294, 558.1205941591197, 386.1457769619055, 599.3348404311233, 612.877818416905, 449.72609150877395, 298.9784986035608, 483.258561722503, 255.84793340599842, 255.1489717178991, 434.7417273581025, 488.645440402544, 323.2598900922538, 519.1083910248276, 428.75725094511733, 447.3655877017358, 933.5586385787474, 1292.1081637847835, 1383.2926515727406, 1355.8971025088451, 1055.312106424995, 825.811833913683, 387.74614459132033, 546.6069423214266, 426.1280984250644, 379.2717488199495, 524.3294900359895, 267.85927511334233, 222.12428230283805, 229.70987971677286, 410.7419098238654, 205.1440378456128, 366.0901091357701, 523.3683857637286, 447.3655877017358, 954.6393576563172, 1929.4283950170275, 1232.861491227647, 1219.1244461195154, 1175.6911655015595, 1489.8367163701364], \"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\"]}};\n",
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      "text/plain": [
       "PreparedData(topic_coordinates=            Freq  cluster  topics         x         y\n",
       "topic                                                \n",
       "0      14.357252        1       1  0.014664  0.005799\n",
       "4      13.233860        1       2  0.056482  0.065233\n",
       "5      12.933760        1       3  0.040293 -0.060175\n",
       "1      11.316494        1       4 -0.222410 -0.076387\n",
       "8      10.693920        1       5  0.078358  0.151408\n",
       "3      10.532171        1       6  0.151155 -0.101375\n",
       "6       7.560320        1       7  0.253692  0.048936\n",
       "2       7.418161        1       8 -0.101996 -0.057984\n",
       "9       6.489072        1       9 -0.205223  0.225435\n",
       "7       5.464990        1      10 -0.065015 -0.200889, topic_info=     Category         Freq          Term        Total  loglift  logprob\n",
       "term                                                                   \n",
       "179   Default  2065.000000          love  2065.000000  15.0000  15.0000\n",
       "279   Default  1945.000000         story  1945.000000  14.0000  14.0000\n",
       "195   Default  2565.000000         movie  2565.000000  13.0000  13.0000\n",
       "117   Default  1355.000000         funny  1355.000000  12.0000  12.0000\n",
       "55    Default  1055.000000        comedy  1055.000000  11.0000  11.0000\n",
       "107   Default  1895.000000          film  1895.000000  10.0000  10.0000\n",
       "162   Default   825.000000         laugh   825.000000   9.0000   9.0000\n",
       "263   Default  1175.000000          show  1175.000000   8.0000   8.0000\n",
       "85    Default   681.000000       episode   681.000000   7.0000   7.0000\n",
       "257   Default   669.000000        series   669.000000   6.0000   6.0000\n",
       "169   Default   768.000000          life   768.000000   5.0000   5.0000\n",
       "217   Default   680.000000   performance   680.000000   4.0000   4.0000\n",
       "245   Default   619.000000          role   619.000000   3.0000   3.0000\n",
       "223   Default   798.000000          play   798.000000   2.0000   2.0000\n",
       "286   Default   652.000000          tell   652.000000   1.0000   1.0000\n",
       "186    Topic1   163.964151         maybe   211.102332   1.6882  -4.8415\n",
       "228    Topic1   242.612464        pretty   317.111517   1.6731  -4.4497\n",
       "26     Topic1   309.015145           bad   419.511014   1.6352  -4.2077\n",
       "247    Topic1   446.859755           say   643.700289   1.5759  -3.8389\n",
       "19     Topic1   145.014644      anything   227.330473   1.4913  -4.9643\n",
       "237    Topic1   522.341314        really   828.464707   1.4797  -3.6828\n",
       "288    Topic1   337.092842         thing   543.542192   1.4632  -4.1208\n",
       "187    Topic1   156.417072          mean   253.055665   1.4598  -4.8886\n",
       "289    Topic1   523.237842         think   850.034573   1.4557  -3.6811\n",
       "270    Topic1   229.367808     something   393.079793   1.4022  -4.5058\n",
       "7      Topic1   198.654126      actually   342.681974   1.3957  -4.6496\n",
       "79     Topic1   105.638211          else   183.898452   1.3866  -5.2811\n",
       "283    Topic1   159.592233          sure   301.191519   1.3058  -4.8685\n",
       "178    Topic1   276.957480           lot   523.368386   1.3045  -4.3173\n",
       "124    Topic1   197.261326           guy   379.271749   1.2872  -4.6566\n",
       "...       ...          ...           ...          ...      ...      ...\n",
       "99     Topic9   133.490184        family   488.645440   1.4374  -4.2530\n",
       "40     Topic9    88.167362           buy   323.259890   1.4358  -4.6678\n",
       "14     Topic9   135.841950        always   519.108391   1.3944  -4.2355\n",
       "18     Topic9   109.917708        anyone   428.757251   1.3739  -4.4473\n",
       "114    Topic9   106.632712        friend   447.365588   1.3011  -4.4776\n",
       "326    Topic9   153.541222         would   933.558639   0.9300  -4.1130\n",
       "313    Topic9   152.050154         watch  1292.108164   0.5952  -4.1228\n",
       "123    Topic9   149.202780         great  1383.292652   0.5081  -4.1417\n",
       "117   Topic10  1355.897103         funny  1355.897103   2.9068  -1.7630\n",
       "55    Topic10   980.942418        comedy  1055.312106   2.8337  -2.0867\n",
       "162   Topic10   765.627217         laugh   825.811834   2.8311  -2.3345\n",
       "146   Topic10   254.685511         humor   387.746145   2.4865  -3.4352\n",
       "116   Topic10   235.869802           fun   546.606942   2.0664  -3.5120\n",
       "192   Topic10   148.673313        moment   426.128098   1.8538  -3.9735\n",
       "124   Topic10   101.631419           guy   379.271749   1.5899  -4.3539\n",
       "172   Topic10   117.008950          line   524.329490   1.4069  -4.2130\n",
       "84    Topic10    55.037206  entertaining   267.859275   1.3244  -4.9672\n",
       "170   Topic10    43.447286         light   222.124282   1.2751  -5.2037\n",
       "275   Topic10    44.431247         stand   229.709880   1.2639  -5.1813\n",
       "52    Topic10    75.998552       classic   410.741910   1.2196  -4.6445\n",
       "136   Topic10    37.582918           hit   205.144038   1.2096  -5.3487\n",
       "261   Topic10    61.192069         short   366.090109   1.1179  -4.8612\n",
       "178   Topic10    86.999816           lot   523.368386   1.1124  -4.5093\n",
       "114   Topic10    63.780641        friend   447.365588   0.9589  -4.8198\n",
       "118   Topic10    93.961029           get   954.639358   0.5884  -4.4324\n",
       "207   Topic10   110.935070           not  1929.428395   0.0508  -4.2663\n",
       "181   Topic10    90.974581          make  1232.861491   0.3003  -4.4647\n",
       "293   Topic10    81.488121          time  1219.124446   0.2014  -4.5748\n",
       "263   Topic10    79.633141          show  1175.691166   0.2146  -4.5978\n",
       "211   Topic10    83.111443           one  1489.836716   0.0206  -4.5551\n",
       "\n",
       "[248 rows x 6 columns], token_table=      Topic      Freq       Term\n",
       "term                            \n",
       "0         1  0.132356         10\n",
       "0         2  0.022529         10\n",
       "0         3  0.163333         10\n",
       "0         4  0.492814         10\n",
       "0         6  0.033793         10\n",
       "0         7  0.022529         10\n",
       "0         8  0.101379         10\n",
       "0        10  0.028161         10\n",
       "3         1  0.035409     acting\n",
       "3         3  0.143501     acting\n",
       "3         4  0.432368     acting\n",
       "3         6  0.240411     acting\n",
       "3         7  0.147229     acting\n",
       "5         4  0.144129      actor\n",
       "5         5  0.015244      actor\n",
       "5         6  0.823198      actor\n",
       "5         8  0.013859      actor\n",
       "5         9  0.002772      actor\n",
       "6         2  0.035622    actress\n",
       "6         4  0.060557    actress\n",
       "6         6  0.744495    actress\n",
       "6         9  0.156736    actress\n",
       "7         1  0.580713   actually\n",
       "7         2  0.087545   actually\n",
       "7         3  0.125481   actually\n",
       "7         4  0.023345   actually\n",
       "7         6  0.014591   actually\n",
       "7         7  0.011673   actually\n",
       "7         8  0.070036   actually\n",
       "7        10  0.090463   actually\n",
       "...     ...       ...        ...\n",
       "321       3  0.094962  wonderful\n",
       "321       4  0.066294  wonderful\n",
       "321       5  0.035835  wonderful\n",
       "321       6  0.218591  wonderful\n",
       "321       7  0.060919  wonderful\n",
       "321       8  0.003583  wonderful\n",
       "321       9  0.519601  wonderful\n",
       "323       1  0.090976       work\n",
       "323       2  0.172205       work\n",
       "323       3  0.297297       work\n",
       "323       5  0.126717       work\n",
       "323       6  0.290799       work\n",
       "323      10  0.022744       work\n",
       "324       3  0.036764      world\n",
       "324       5  0.964523      world\n",
       "326       1  0.414543      would\n",
       "326       2  0.035349      would\n",
       "326       3  0.109259      would\n",
       "326       4  0.093192      would\n",
       "326       5  0.051416      would\n",
       "326       8  0.131754      would\n",
       "326       9  0.164960      would\n",
       "328       2  0.365776       year\n",
       "328       3  0.182888       year\n",
       "328       4  0.260651       year\n",
       "328       8  0.191528       year\n",
       "330       2  0.685172      young\n",
       "330       6  0.115952      young\n",
       "330       7  0.044273      young\n",
       "330       9  0.153900      young\n",
       "\n",
       "[1027 rows x 3 columns], R=15, lambda_step=0.01, plot_opts={'ylab': 'PC2', 'xlab': 'PC1'}, topic_order=[1, 5, 6, 2, 9, 4, 7, 3, 10, 8])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.sklearn.prepare(pos_nmf, ptvf_features, ptvf, R=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Display and visualize topics for negative reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic #1 without weights\n",
      "['get', 'go', 'kill', 'guy', 'scene', 'take', 'end', 'back', 'start', 'around', 'look', 'one', 'thing', 'come', 'first']\n",
      "\n",
      "Topic #2 without weights\n",
      "['bad', 'movie', 'ever', 'acting', 'see', 'terrible', 'one', 'plot', 'effect', 'awful', 'not', 'even', 'make', 'horrible', 'special']\n",
      "\n",
      "Topic #3 without weights\n",
      "['film', 'make', 'not', 'see', 'would', 'director', 'one', 'many', 'may', 'bad', 'however', 'horror', 'no', 'say', 'feel']\n",
      "\n",
      "Topic #4 without weights\n",
      "['character', 'story', 'book', 'plot', 'main', 'seem', 'no', 'interesting', 'not', 'movie', 'read', 'end', 'feel', 'nothing', 'original']\n",
      "\n",
      "Topic #5 without weights\n",
      "['movie', 'think', 'would', 'not', 'like', 'say', 'watch', 'could', 'see', 'really', 'people', 'good', 'know', 'want', 'make']\n",
      "\n",
      "Topic #6 without weights\n",
      "['funny', 'comedy', 'laugh', 'joke', 'try', 'not', 'stupid', 'suppose', 'moment', 'fun', 'even', 'black', 'guy', 'character', 'really']\n",
      "\n",
      "Topic #7 without weights\n",
      "['actor', 'play', 'good', 'cast', 'role', 'performance', 'script', 'much', 'great', 'star', 'act', 'look', 'well', 'give', 'director']\n",
      "\n",
      "Topic #8 without weights\n",
      "['man', 'woman', 'old', 'young', 'year', 'life', 'love', 'girl', 'child', 'play', 'sex', 'wife', 'family', 'boy', 'kid']\n",
      "\n",
      "Topic #9 without weights\n",
      "['show', 'tv', 'series', 'watch', 'not', 'original', 'people', 'like', 'every', 'new', 'kid', 'us', 'make', 'use', 'american']\n",
      "\n",
      "Topic #10 without weights\n",
      "['waste', 'time', 'money', 'watch', 'minute', 'hour', 'movie', 'spend', 'not', 'life', 'save', 'even', 'worth', 'back', 'crap']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# build topic model on negative sentiment review features\n",
    "neg_nmf = NMF(n_components=10, \n",
    "          random_state=42, alpha=0.1, l1_ratio=0.2)\n",
    "neg_nmf.fit(ntvf_features)      \n",
    "# extract features and component weights\n",
    "neg_feature_names = ntvf.get_feature_names()\n",
    "neg_weights = neg_nmf.components_\n",
    "# extract and display topics and their components\n",
    "neg_topics = tmu.get_topics_terms_weights(neg_weights, neg_feature_names)\n",
    "tmu.print_topics_udf(topics=neg_topics,\n",
    "                 total_topics=total_topics,\n",
    "                 num_terms=15,\n",
    "                 display_weights=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el638028532673648649979675126\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el638028532673648649979675126_data = {\"topic.order\": [1, 5, 3, 4, 2, 7, 8, 10, 9, 6], \"lambda.step\": 0.01, \"token.table\": {\"Freq\": [0.05807535000181307, 0.19442617174520027, 0.08080048695904427, 0.2575515521819536, 0.0025250152174701333, 0.04797528913193253, 0.303001826096416, 0.020200121739761066, 0.037875228262052, 0.010150527308422124, 0.08458772757018437, 0.060903163850532745, 0.26391371001897523, 0.42293863785092184, 0.059211409299129054, 0.06936193660755118, 0.027068072822459, 0.07652214425109839, 0.06801968377875413, 0.7968020099796912, 0.041297665151386435, 0.017004920944688532, 0.14470351664251088, 0.7632150081321812, 0.09091990868688737, 0.1965831166726314, 0.26088600530386596, 0.13228022804139683, 0.12125687570461376, 0.04593063473659612, 0.0532795362944515, 0.06062843785230688, 0.13044300265193298, 0.11947929847601214, 0.029869824619003036, 0.1851929126378188, 0.05077870185230516, 0.09558343878080972, 0.22701066710442308, 0.27181540403292764, 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       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el638028532673648649979675126\", ldavis_el638028532673648649979675126_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el638028532673648649979675126\", ldavis_el638028532673648649979675126_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el638028532673648649979675126\", ldavis_el638028532673648649979675126_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=            Freq  cluster  topics         x         y\n",
       "topic                                                \n",
       "0      15.159817        1       1  0.076605 -0.021691\n",
       "4      13.708854        1       2 -0.060157 -0.026393\n",
       "2      11.157697        1       3  0.001907 -0.021786\n",
       "3      10.813738        1       4  0.076284  0.049930\n",
       "1      10.635604        1       5 -0.231589  0.020158\n",
       "6      10.582598        1       6  0.107173  0.039257\n",
       "7       8.862276        1       7  0.218109 -0.142682\n",
       "9       7.120793        1       8 -0.242084 -0.125559\n",
       "8       6.419647        1       9  0.053506 -0.048141\n",
       "5       5.538976        1      10  0.000245  0.276907, topic_info=     Category         Freq       Term        Total  loglift  logprob\n",
       "term                                                                \n",
       "107   Default  2236.000000       film  2236.000000  15.0000  15.0000\n",
       "260   Default  1356.000000       show  1356.000000  14.0000  14.0000\n",
       "117   Default  1240.000000      funny  1240.000000  13.0000  13.0000\n",
       "28    Default  1776.000000        bad  1776.000000  12.0000  12.0000\n",
       "310   Default  1111.000000      waste  1111.000000  11.0000  11.0000\n",
       "55    Default   893.000000     comedy   893.000000  10.0000  10.0000\n",
       "289   Default  1399.000000       time  1399.000000   9.0000   9.0000\n",
       "189   Default  2357.000000      movie  2357.000000   8.0000   8.0000\n",
       "157   Default   799.000000      laugh   799.000000   7.0000   7.0000\n",
       "49    Default  1084.000000  character  1084.000000   6.0000   6.0000\n",
       "176   Default   791.000000        man   791.000000   5.0000   5.0000\n",
       "275   Default   930.000000      story   930.000000   4.0000   4.0000\n",
       "148   Default   642.000000       joke   642.000000   3.0000   3.0000\n",
       "187   Default   667.000000      money   667.000000   2.0000   2.0000\n",
       "317   Default   639.000000      woman   639.000000   1.0000   1.0000\n",
       "151    Topic1   296.182894       kill   318.826975   1.8129  -4.3284\n",
       "130    Topic1   162.230095       head   221.157364   1.5767  -4.9303\n",
       "67     Topic1   144.792485       dead   201.323472   1.5569  -5.0441\n",
       "140    Topic1   139.108803      house   200.283826   1.5220  -5.0841\n",
       "243    Topic1   192.881583        run   279.680171   1.5150  -4.7573\n",
       "20     Topic1   218.199939     around   324.167125   1.4907  -4.6339\n",
       "257    Topic1   151.465034      shoot   228.512367   1.4753  -4.9990\n",
       "125    Topic1   295.348838        guy   483.584801   1.3935  -4.3312\n",
       "272    Topic1   224.884183      start   382.346878   1.3558  -4.6038\n",
       "65     Topic1   106.941458        cut   182.422547   1.3525  -5.3471\n",
       "72     Topic1   125.686888        die   222.527374   1.3153  -5.1856\n",
       "308    Topic1   102.504916       walk   188.514620   1.2773  -5.3894\n",
       "106    Topic1   111.576802      fight   209.403931   1.2570  -5.3046\n",
       "108    Topic1    87.375183    finally   165.255309   1.2492  -5.5491\n",
       "68     Topic1   114.935979      death   217.874857   1.2470  -5.2750\n",
       "...       ...          ...        ...          ...      ...      ...\n",
       "304    Topic9    59.923876    version   294.737118   1.1528  -5.0670\n",
       "303    Topic9    93.310733        use   480.903906   1.1061  -4.6241\n",
       "167    Topic9    63.154657       live   327.356586   1.1003  -5.0145\n",
       "325    Topic9    58.315564     writer   305.496884   1.0897  -5.0942\n",
       "211    Topic9   121.985745     people   743.555845   0.9383  -4.3562\n",
       "311    Topic9   154.980332      watch  1342.737374   0.5866  -4.1168\n",
       "164    Topic9   119.715163       like  1298.007227   0.3623  -4.3750\n",
       "201    Topic9   127.169026        not  2356.114616  -0.1734  -4.3146\n",
       "177    Topic9    82.434764       many   631.931797   0.7090  -4.7481\n",
       "175    Topic9    98.653405       make  1477.664778   0.0392  -4.5685\n",
       "117   Topic10  1240.046669      funny  1240.046669   2.8934  -1.8896\n",
       "55    Topic10   854.231692     comedy   893.748710   2.8481  -2.2623\n",
       "148   Topic10   575.233228       joke   642.217885   2.7832  -2.6577\n",
       "157   Topic10   647.703989      laugh   799.954468   2.6822  -2.5391\n",
       "278   Topic10   122.392615    suppose   364.124516   1.8031  -4.2053\n",
       "116   Topic10   102.499123        fun   323.877127   1.7429  -4.3827\n",
       "186   Topic10   107.874050     moment   348.136178   1.7217  -4.3316\n",
       "276   Topic10   151.737857     stupid   500.721533   1.6995  -3.9904\n",
       "36    Topic10    84.747293      black   308.246395   1.6021  -4.5728\n",
       "296   Topic10   178.486645        try   670.578374   1.5697  -3.8280\n",
       "99    Topic10    71.848210       fail   344.587750   1.3256  -4.7380\n",
       "129   Topic10    68.522321       hard   376.597156   1.1893  -4.7854\n",
       "100   Topic10    57.221689       fall   336.864447   1.1206  -4.9656\n",
       "35    Topic10    55.340713        bit   346.216947   1.0598  -4.9990\n",
       "125   Topic10    76.891073        guy   483.584801   1.0545  -4.6701\n",
       "201   Topic10   172.777890        not  2356.114616   0.2806  -3.8605\n",
       "6     Topic10    71.154300   actually   544.299031   0.8587  -4.7477\n",
       "88    Topic10    86.292079       even  1179.687172   0.2781  -4.5548\n",
       "49    Topic10    76.267272  character  1084.332502   0.2389  -4.6783\n",
       "232   Topic10    72.663809     really   924.396363   0.3501  -4.7267\n",
       "\n",
       "[239 rows x 6 columns], token_table=      Topic      Freq      Term\n",
       "term                           \n",
       "0         1  0.058075        10\n",
       "0         2  0.194426        10\n",
       "0         3  0.080800        10\n",
       "0         5  0.257552        10\n",
       "0         6  0.002525        10\n",
       "0         7  0.047975        10\n",
       "0         8  0.303002        10\n",
       "0         9  0.020200        10\n",
       "0        10  0.037875        10\n",
       "2         1  0.010151       act\n",
       "2         2  0.084588       act\n",
       "2         3  0.060903       act\n",
       "2         5  0.263914       act\n",
       "2         6  0.422939       act\n",
       "2         7  0.059211       act\n",
       "2         8  0.069362       act\n",
       "2         9  0.027068       act\n",
       "3         3  0.076522    acting\n",
       "3         4  0.068020    acting\n",
       "3         5  0.796802    acting\n",
       "3         6  0.041298    acting\n",
       "3         8  0.017005    acting\n",
       "5         5  0.144704     actor\n",
       "5         6  0.763215     actor\n",
       "5         8  0.090920     actor\n",
       "6         1  0.196583  actually\n",
       "6         2  0.260886  actually\n",
       "6         3  0.132280  actually\n",
       "6         5  0.121257  actually\n",
       "6         6  0.045931  actually\n",
       "...     ...       ...       ...\n",
       "323       2  0.597173     would\n",
       "323       3  0.214213     would\n",
       "323       6  0.023607     would\n",
       "323       7  0.065575     would\n",
       "323       8  0.023607     would\n",
       "323       9  0.064701     would\n",
       "325       1  0.075287    writer\n",
       "325       2  0.052374    writer\n",
       "325       3  0.003273    writer\n",
       "325       4  0.360069    writer\n",
       "325       6  0.193128    writer\n",
       "325       8  0.039280    writer\n",
       "325       9  0.189855    writer\n",
       "325      10  0.085107    writer\n",
       "326       1  0.226257     wrong\n",
       "326       2  0.497023     wrong\n",
       "326       3  0.063055     wrong\n",
       "326       4  0.018546     wrong\n",
       "326       5  0.018546     wrong\n",
       "326       6  0.051928     wrong\n",
       "326       9  0.066764     wrong\n",
       "326      10  0.063055     wrong\n",
       "327       2  0.074992      year\n",
       "327       3  0.091490      year\n",
       "327       5  0.080991      year\n",
       "327       6  0.011999      year\n",
       "327       7  0.643428      year\n",
       "327       9  0.095989      year\n",
       "330       6  0.042668     young\n",
       "330       7  0.959016     young\n",
       "\n",
       "[1030 rows x 3 columns], R=15, lambda_step=0.01, plot_opts={'ylab': 'PC2', 'xlab': 'PC1'}, topic_order=[1, 5, 3, 4, 2, 7, 8, 10, 9, 6])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pyLDAvis.sklearn.prepare(neg_nmf, ntvf_features, ntvf, R=15)"
   ]
  }
 ],
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